Faculty Bibliography

Pigmented vulvar lesions are present in approximately 1 in 10 women and include melanocytic and nonmelanocytic proliferations. Vulvar nevi, melanosis, and melanoma are particularly challenging because of the similarity of their clinical and/or histopathological presentation. As a result, they are often difficult to diagnose, may result in patient and physician anxiety, and can lead to unneeded, potentially disfiguring surgical procedures. Because it is often detected late, vulvar melanoma carries a poor prognosis with associated significant morbidity and mortality, underscoring the importance of prompt recognition and treatment. In this review, we analyze the distinct epidemiologic, clinical, and histopathologic characteristics of vulvar nevi, melanosis, and melanoma, discuss treatment options, and propose a practical, systematic approach to facilitate formulation of a differential diagnosis and initiation of appropriate management.

Although personal melanoma risk factors are well established, the contribution of socioeconomic factors, including clothing styles, social norms, medical paradigms, perceptions of tanned skin, economic trends, and travel patterns, to melanoma incidence has not been fully explored. We analyzed artwork, advertisements, fashion trends, and data regarding leisure-time activities to estimate historical changes in UV skin exposure. We used data from national cancer registries to compare melanoma incidence rates with estimated skin exposure and found that they rose in parallel. Although firm conclusions about melanoma causation cannot be made in an analysis such as this, we provide a cross-disciplinary, historical framework in which to consider public health and educational measures that may ultimately help reverse melanoma incidence trends. (Am J Public Health. Published online ahead of print September 11, 2014: e1-e8. doi:10.2105/AJPH.2014.302185).

BACKGROUND: Identifying individuals at increased risk for melanoma could potentially improve public health through targeted surveillance and early detection. Studies have separately demonstrated significant associations between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. Existing melanoma risk prediction models do not include these factors; therefore, we investigated their potential to improve the performance of a risk model. METHODS: Using 875 melanoma cases and 765 controls from the population-based Minnesota Skin Health Study we compared the predictive ability of a clinical melanoma risk model (Model A) to an enhanced model (Model F) using receiver operating characteristic (ROC) curves. Model A used self-reported conventional risk factors including mole phenotype categorized as "none", "few", "some" or "many" moles. Model F added MC1R genotype and measures of indoor and outdoor UV exposure to Model A. We also assessed the predictive ability of these models in subgroups stratified by mole phenotype (e.g. nevus-resistant ("none" and "few" moles) and nevus-prone ("some" and "many" moles)). RESULTS: Model A (the reference model) yielded an area under the ROC curve (AUC) of 0.72 (95% CI = 0.69, 0.74). Model F was improved with an AUC = 0.74 (95% CI = 0.71-0.76, p<0.01). We also observed substantial variations in the AUCs of Models A & F when examined in the nevus-prone and nevus-resistant subgroups. CONCLUSIONS: These results demonstrate that adding genotypic information and environmental exposure data can increase the predictive ability of a clinical melanoma risk model, especially among nevus-prone individuals.

BACKGROUND: Steadily high melanoma mortality rates urge for the availability of novel biomarkers with a more personalized ability to predict melanoma clinical outcomes. Germline risk variants are promising candidates for this purpose; however, their prognostic potential in melanoma has never been systematically tested. METHODS: We examined the effect of 108 melanoma susceptibility single nucleotide polymorphisms (SNPs), associated in recent GWAS with melanoma and melanoma-related phenotypes, on recurrence-free survival (RFS) and overall survival (OS), in 891 prospectively accrued melanoma patients. Cox proportional hazards models (Cox PH) were used to test the associations between 108 melanoma risk SNPs and RFS and OS adjusted by age at diagnosis, gender, tumor stage, histological subtype and other primary tumor characteristics. RESULTS: We identified significant associations for rs7538876 (RCC2) with RFS (HR = 1.48, 95% CI = 1.20-1.83, p = 0.0005) and rs9960018 (DLGAP1) with both RFS and OS (HR = 1.43, 95% CI = 1.07-1.91, p = 0.01, HR = 1.52, 95% CI = 1.09-2.12, p = 0.01, respectively) using multivariable Cox PH models. In addition, we developed a logistic regression model that incorporates rs7538876, rs9960018, primary tumor histological type and stage at diagnosis that has an improved discriminatory ability to classify 3-year recurrence (AUC = 82%) compared to histological type and stage alone (AUC = 78%). CONCLUSIONS: We identified associations between melanoma risk variants and melanoma outcomes. The significant associations observed for rs7538876 and rs9960018 suggest a biological implication of these loci in melanoma progression. The observed predictive patterns of associated variants with clinical end-points suggest for the first time the potential for utilization of genetic risk markers in melanoma prognostication.

The prognostic value of mitotic rate in melanoma is increasingly recognized, particularly in thin melanoma in which the presence or absence of a single mitosis/mm can change staging from T1a to T1b. Still, accurate mitotic rate calculation (mitoses/mm) on hematoxylin and eosin (H&E)-stained sections can be challenging. Antimonoclonal mitotic protein-2 (MPM-2) and antiphosphohistone-H3 (PHH3) are 2 antibodies reported to be more mitosis-specific than other markers of proliferation such as Ki-67. We used light microscopy and computer-assisted image analysis software to quantify MPM-2 and PHH3 staining in melanoma. We then compared mitotic rates by each method with conventional H&E-based mitotic rate for correlation with clinical outcomes. Our study included primary tissues from 190 nonconsecutive cutaneous melanoma patients who were prospectively enrolled at New York University Langone Medical Center with information on age, gender, and primary tumor characteristics. The mitotic rate was quantified manually by light microscopy of corresponding H&E-stained, MPM-2-stained, and PHH3-stained sections. Computer-assisted image analysis was then used to quantify immunolabeled mitoses on the previously examined PHH3 and MPM-2 slides. We then analyzed the association between mitotic rate and both progression-free and melanoma-specific survival. Univariate analysis of PHH3 found significant correlation between increased PHH3 mitotic rate and decreased progression-free survival (P=0.04). Computer-assisted image analysis enhanced the correlation of PHH3 mitotic rate with progression-free survival (P=0.02). Regardless of the detection method, neither MPM-2 nor PHH3 offered significant advantage over conventional H&E determination of mitotic rate.